SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 926–950 of 3874 papers

TitleStatusHype
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural NetworksCode1
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder ArchitectureCode1
Cross-Scale Internal Graph Neural Network for Image Super-ResolutionCode1
SRFlow: Learning the Super-Resolution Space with Normalizing FlowCode1
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection NetworksCode1
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual LearningCode1
Exploring Sparsity in Image Super-Resolution for Efficient InferenceCode1
AutoGAN-Distiller: Searching to Compress Generative Adversarial NetworksCode1
iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networksCode1
Super-resolution Variational Auto-EncodersCode1
Neural Sparse Representation for Image RestorationCode1
Learning Texture Transformer Network for Image Super-ResolutionCode1
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars MiningCode1
Unsupervised Adaptation Learning for Hyperspectral Imagery Super-ResolutionCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Robust Reference-Based Super-Resolution With Similarity-Aware Deformable ConvolutionCode1
Joint Demosaicing and Denoising With Self GuidanceCode1
TDAN: Temporally-Deformable Alignment Network for Video Super-ResolutionCode1
Perceptual Extreme Super Resolution Network with Receptive Field BlockCode1
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial NetworkCode1
Iterative Network for Image Super-ResolutionCode1
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral ImageryCode1
MedSRGAN: medical images super-resolution using generative adversarial networksCode1
Super-Resolution Domain Adaptation Networks for Semantic Segmentation via Pixel and Output Level AligningCode1
Invertible Image RescalingCode1
Show:102550
← PrevPage 38 of 155Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified